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   "source": [
    "<center>\n",
    "<img src=\"../../img/ods_stickers.jpg\">\n",
    "    \n",
    "## [mlcourse.ai](https://mlcourse.ai) - Open Machine Learning Course\n",
    "\n",
    "Author: [Yury Kashnitsky](https://www.linkedin.com/in/festline/). Translated and edited by [Sergey Isaev](https://www.linkedin.com/in/isvforall/), [Artem Trunov](https://www.linkedin.com/in/datamove/), [Anastasia Manokhina](https://www.linkedin.com/in/anastasiamanokhina/), and [Yuanyuan Pao](https://www.linkedin.com/in/yuanyuanpao/). All content is distributed under the [Creative Commons CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center> Assignment #1 (demo)\n",
    "## <center>  Exploratory data analysis with Pandas\n",
    "\n",
    "\n",
    "**Same assignment as a [Kaggle Kernel](https://www.kaggle.com/kashnitsky/a1-demo-pandas-and-uci-adult-dataset) + [solution](https://www.kaggle.com/kashnitsky/a1-demo-pandas-and-uci-adult-dataset-solution).**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In this task you should use Pandas to answer a few questions about the [Adult](https://archive.ics.uci.edu/ml/datasets/Adult) dataset. (You don't have to download the data – it's already  in the repository). Choose the answers in the [web-form](https://docs.google.com/forms/d/1uY7MpI2trKx6FLWZte0uVh3ULV4Cm_tDud0VDFGCOKg).**"
   ]
  },
  {
   "cell_type": "markdown",
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    "Unique values of all features (for more information, please see the links above):\n",
    "- `age`: continuous.\n",
    "- `workclass`: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\n",
    "- `fnlwgt`: continuous.\n",
    "- `education`: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\n",
    "- `education-num`: continuous.\n",
    "- `marital-status`: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\n",
    "- `occupation`: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\n",
    "- `relationship`: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\n",
    "- `race`: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\n",
    "- `sex`: Female, Male.\n",
    "- `capital-gain`: continuous.\n",
    "- `capital-loss`: continuous.\n",
    "- `hours-per-week`: continuous.\n",
    "- `native-country`: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.   \n",
    "- `salary`: >50K,<=50K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "pd.set_option(\"display.max.columns\", 100)\n",
    "# to draw pictures in jupyter notebook\n",
    "%matplotlib inline\n",
    "# we don't like warnings\n",
    "# you can comment the following 2 lines if you'd like to\n",
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"../../data/adult.data.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. How many men and women (*sex* feature) are represented in this dataset?** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. What is the average age (*age* feature) of women?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. What is the percentage of German citizens (*native-country* feature)?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4-5. What are the mean and standard deviation of age for those who earn more than 50K per year (*salary* feature) and those who earn less than 50K per year? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6. Is it true that people who earn more than 50K have at least high school education? (*education – Bachelors, Prof-school, Assoc-acdm, Assoc-voc, Masters* or *Doctorate* feature)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**7. Display age statistics for each race (*race* feature) and each gender (*sex* feature). Use *groupby()* and *describe()*. Find the maximum age of men of *Amer-Indian-Eskimo* race.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8. Among whom is the proportion of those who earn a lot (>50K) greater: married or single men (*marital-status* feature)? Consider as married those who have a *marital-status* starting with *Married* (Married-civ-spouse, Married-spouse-absent or Married-AF-spouse), the rest are considered bachelors.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**9. What is the maximum number of hours a person works per week (*hours-per-week* feature)? How many people work such a number of hours, and what is the percentage of those who earn a lot (>50K) among them?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**10. Count the average time of work (*hours-per-week*) for those who earn a little and a lot (*salary*) for each country (*native-country*). What will these be for Japan?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You code here"
   ]
  }
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